Training a machine-learning model to effectively perform a task, such as image classification, involves showing the model thousands, millions, or even billions of example images. Gathering such enormous datasets can be especially challenging when privacy is a concern, such as with medical images. Researchers from MIT and the MIT-born startup DynamoFL have now taken one popular solution to this problem, known as federated learning, and made it faster and more accurate.
Collaborative machine learning that preserves privacy
Reader’s Picks
-
Over the last few years, the pandemic has forced most of us to stay home in our own neighborhoods. New [...]
-
A new, international analysis links belief in COVID-19 conspiracy theories to a lower tendency to support and participate in public [...]
-
For many young people, retirement is a blip on the radar, if not a total unknown. This is particularly true [...]